Synthetic aperture radar (SAR) imagery has received a great deal of attention in recent years due to the deployment of many cutting edge spaceborne radar systems providing
high resolution imagery. However, severe image distortion is a
critical problem, and this is often a result of radio frequency
interference (RFI) and noise. Issues that arise from distortion
include missing detection and inaccurate height maps.
SAR images are particularly important for classification and
automatic target recognition (ATR) tasks. For such applications,
access to comprehensive databases of SAR images as well as
SAR images contaminated with RFI and noise is critical to
enable the effective training and optimisation of classification
algorithms and to provide a common baseline for benchmarking
purposes. Given these challenges, the purpose of this paper
is to show that neural style transfer can be used to induce
RFI and noise into SAR images. We can also further classify
the type of contamination using image classification techniques.
The experimental data is shown to verify the efficiency of our
approach.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done